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- Amazon (United States)US32 papers
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7 papers · 1 filter
GROMACS in the cloud: A global supercomputer to speed up alchemical drug design
Carsten Kutzner, Christian Kniep, Austin Cherian +4
We assess costs and efficiency of state-of-the-art high performance cloud computing compared to a traditional on-premises compute cluster. Our use case are atomistic simulations ca…
Model-based Reinforcement Learning for Service Mesh Fault Resiliency in a Web Application-level
Fanfei Meng, Lalita Jagadeesan, Marina Thottan
Microservice-based architectures enable different aspects of web applications to be created and updated independently, even after deployment. Associated technologies such as servic…
Tuna: A Static Analysis Approach to Optimizing Deep Neural Networks
Yao Wang, Xingyu Zhou, Yanming Wang +3
We introduce Tuna, a static analysis approach to optimizing deep neural network programs. The optimization of tensor operations such as convolutions and matrix multiplications is t…
Sketch and Scale: Geo-distributed tSNE and UMAP
Viska Wei, Nikita Ivkin, Vladimir Braverman +1
Running machine learning analytics over geographically distributed datasets is a rapidly arising problem in the world of data management policies ensuring privacy and data security…
A simple and effective predictive resource scaling heuristic for large-scale cloud applications
Valentin Flunkert, Quentin Rebjock, Joel Castellon +2
We propose a simple yet effective policy for the predictive auto-scaling of horizontally scalable applications running in cloud environments, where compute resources can only be ad…
DeCaf: Diagnosing and Triaging Performance Issues in Large-Scale Cloud Services
Chetan Bansal, Sundararajan Renganathan, Ashima Asudani +2
Large scale cloud services use Key Performance Indicators (KPIs) for tracking and monitoring performance. They usually have Service Level Objectives (SLOs) baked into the customer…